%{search_type} search results

3 catalog results

RSS feed for this result
xxxix, 1,198 p. : ill. ; 24 cm.
  • Part I: The Linear Regression Model Chapter 1: Econometrics Chapter 2: The Linear Regression Model Chapter 3: Least Squares Chapter 4: The Least Squares Estimator Chapter 5: Hypothesis Tests and Model Selection Chapter 6: Functional Form and Structural Change Chapter 7: Nonlinear, Semiparametric, and Nonparametric Regression Models Chapter 8: Endogeneity and Instrumental Variable Estimation Part II: Generalized Regression Model and Equation Systems Chapter 9: The Generalized Regression Model and Heteroscedasticity Chapter 10: Systems of Equations Chapter 11: Models for Panel Data Part III: Estimation Methodology Chapter 12: Estimation Frameworks in Econometrics Chapter 13: Minimum Distance Estimation and the Generalized Method of Moments Chapter 14: Maximum Likelihood Estimation Chapter 15: Simulation-Based Estimation and Inference Chapter 16: Bayesian Estimation and Inference Part IV: Cross Sections, Panel Data, and Microeconometrics Chapter 17: Discrete Choice Chapter 18: Discrete Choices and Event Counts Chapter 19: Limited Dependent Variables--Truncation, Censoring, and Sample Selection Part V: Time Series and Macroeconometrics Chapter 20: Serial Correlation Chapter 21: Models with Lagged Variables Chapter 22: Time-Series Models Chapter 23: Nonstationary Data Part VI: Appendices Appendix A: Matrix Algebra Appendix B: Probability and Distribution Theory Appendix C: Estimation and Inference Appendix D: Large-Sample Distribution Theory Appendix E: Computation and Optimization Appendix F: Data Sets Used in Applications Appendix G: Statistical Tables.
  • (source: Nielsen Book Data)9780131395381 20160606
Econometric Analysis serves as a bridge between an introduction to the field of econometrics and the professional literature for social scientists and other professionals in the field of social sciences, focusing on applied econometrics and theoretical background. This book provides a broad survey of the field of econometrics that allows the reader to move from here to practice in one or more specialized areas. At the same time, the reader will gain an appreciation of the common foundation of all the fields presented and use the tools they employ.
(source: Nielsen Book Data)9780131395381 20160606
Business Library
xiii, 373 p. : ill ; 22 cm.
  • List of Figures vii List of Tables ix Preface xi Acknowledgments xv Organization of This Book xvii PART I: PRELIMINARIES 1 Chapter 1: Questions about Questions 3 Chapter 2: The Experimental Ideal 11 2.1 The Selection Problem 12 2.2 Random Assignment Solves the Selection Problem 15 2.3 Regression Analysis of Experiments 22 PART II: THE CORE 25 Chapter 3: Making Regression Make Sense 27 3.1 Regression Fundamentals 28 3.2 Regression and Causality 51 3.3 Heterogeneity and Nonlinearity 68 3.4 Regression Details 91 3.5 Appendix: Derivation of the Average Derivative Weighting Function 110 Chapter 4: Instrumental Variables in Action: Sometimes You Get What You Need 113 4.1 IV and Causality 115 4.2 Asymptotic 2SLS Inference 138 4.3 Two-Sample IV and Split-Sample IV 147 4.4 IV with Heterogeneous Potential Outcomes 150 4.5 Generalizing LATE 173 4.6 IV Details 188 4.7 Appendix 216 Chapter 5: Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data 221 5.1 Individual Fixed Effects 221 5.2 Differences-in-Differences 227 5.3 Fixed Effects versus Lagged Dependent Variables 243 5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables 246 PART III: EXTENSIONS 249 Chapter 6: Getting a Little Jumpy: Regression Discontinuity Designs 251 6.1 Sharp RD 251 6.2 Fuzzy RD Is IV 259 Chapter 7: Quantile Regression 269 7.1 The Quantile Regression Model 270 7.2 IV Estimation of Quantile Treatment Effects 283 Chapter 8: Nonstandard Standard Error Issues 293 8.1 The Bias of Robust Standard Error Estimates 294 8.2 Clustering and Serial Correlation in Panels 308 8.3 Appendix: Derivation of the Simple Moulton Factor 323 Last Words 327 Acronyms and Abbreviations 329 Empirical Studies Index 335 References 339 Index 361.
  • (source: Nielsen Book Data)9780691120348 20160528
The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages?"Mostly Harmless Econometrics" shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, "Mostly Harmless Econometrics" covers important new extensions - regression-discontinuity designs and quantile regression - as well as how to get standard errors right. Joshua Angrist and Jorn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. This book features: an irreverent review of econometric essentials; focus on tools that applied researchers use most; chapters on regression-discontinuity designs, quantile regression, and standard errors; many empirical examples; and, a clear and concise resource with wide applications.
(source: Nielsen Book Data)9780691120348 20160528
Business Library
MGTECON-604-01, FINANCE-633-01
x, 172 pages : illustrations ; 24 cm
  • Introduction: Identification-- Tolerating Ambiguity. Part 1 Extrapolation: Predicting Criminality-- Probabilistic Prediction-- Inferring Conditional Distributions from Random-Sample Data-- Prior Distributional Information-- Predicting High School Graduation. Part 2 The Selection Problem: The Nature of the Problem-- Identification from Censored Samples Alone-- Bounding the Probability of Exiting Homelessness-- Prior Distributional Information-- Identification of Treatment Effects-- Information Linking Outcomes across Treatments-- Predicting High School Graduation If All Families Were Intact. Part 3 The Mixing Problem in Program Evaluation: The Experimental Evaluation of Social Programs-- Variation in Treatment-- The Perry Preschool Project-- Identification of Mixtures Using Only Knowledge of the Marginals-- Restrictions on the Outcome Distribution-- Restrictions on the Treatment Policy-- Identifying Combinations of Assumptions. Part 4 Response-Based Sampling: The odds Ratio and Public Health-- Bounds on Relative and Attributable Risk-- Information on Marginal Distributions-- Sampling from One Response Stratum-- General Binary Stratifications. Part 5 Predicting Individual Behaviour: Revealed Preference Analysis-- How Do Youth Infer the Returns to Schooling?-- Analysis of Intentions Data. Part 6 Simultaneity: "The" Identification Problem in Econometrics-- The Linear Market Model-- Equilibrium in Games-- Simultaneity with Downward-Sloping Demand. Part 7 The Reflection Problem: Endogenous, Contextual, and Correlated Effects-- A Linear Model-- A Pure Endogenous Effects Model-- Inferring the Composition of Reference Groups-- Dynamic Analysis.
  • (source: Nielsen Book Data)9780674442832 20161213
  • Preface Introduction Identification Tolerating Ambiguity 1 Extrapolation 1.1.
  • (source: Nielsen Book Data)9780674442849 20161213
This text provides a language and a set of tools for finding bounds on the predictions that social and behavioural scientists can logically make from non-experimental and experimental data. Economist Charles Manski draws on examples from criminology, demography, epidemiology, social psychology and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools. There are many traditional ways to present identification problems in econometrics, sociology and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and non-distinct parameter estimates. Manski's strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioural sciences. In each case problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived providing fresh insights. Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He specifies assumptions and develops his non-parametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children's outcomes and the effect of policing on crime rates. Successive chapters deal with topics such as the use of experiments to evaluate social programmes, the use of case-control sampling by epidemiologists studying the association of risk factors and disease and the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighbourhood and contextual effects.
(source: Nielsen Book Data)9780674442832 20161213
Business Library